The idea of branch pruning was first introduced by ResNets [1]} primarily to address the issue of vanishing gradients in deeper networks. The idea being that if neural networks can approximate a complicated function they can also approximate residual function \(f(\mathbf {h}_{l-1}) - \mathbf {h}_{l-1}\)  which can be defined as:
\(\mathbf {h}_{l} = f(\mathbf {h}_{l-1}) + \mathbf {h}_{l-1}.\) 
